Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	46,002
2	South Dakota	40,712
3	Louisiana	39,129
4	Mississippi	37,995
5	Alabama	36,112
6	Florida	35,761
7	Iowa	35,575
8	Tennessee	34,276
9	Arkansas	34,063
10	Wisconsin	33,637
11	South Carolina	32,529
12	Arizona	32,274
13	Idaho	31,864
14	Nebraska	31,681
15	Georgia	31,204
16	Utah	31,055
17	Texas	30,731
18	Nevada	30,202
19	Illinois	28,813
20	Oklahoma	28,426
21	Rhode Island	27,935
22	Missouri	27,586
23	Kansas	26,274
24	New Jersey	25,463
25	New York	25,438
26	North Carolina	24,162
27	Delaware	24,161
28	Montana	24,079
29	District of Columbia	23,431
30	Indiana	23,421
31	Maryland	22,904
32	Minnesota	22,732
33	California	22,687
34	Massachusetts	21,358
35	Kentucky	21,170
36	Virginia	19,928
37	New Mexico	18,779
38	Puerto Rico	18,485
39	Connecticut	18,335
40	Alaska	17,483
41	Wyoming	17,483
42	Michigan	17,032
43	Ohio	16,291
44	Colorado	15,739
45	Pennsylvania	15,107
46	Washington	13,836
47	West Virginia	11,749
48	Hawaii	10,225
49	Oregon	9,679
50	New Hampshire	7,350
51	Maine	4,511
52	Vermont	3,184

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	1,056
2	South Dakota	821
3	Wisconsin	725
4	Montana	696
5	Idaho	526
6	Wyoming	465
7	Iowa	436
8	Nebraska	425
9	Utah	414
10	Rhode Island	392
11	Oklahoma	371
12	Arkansas	353
13	Illinois	333
14	New Mexico	329
15	Kentucky	308
16	Indiana	301
17	Alaska	297
18	Tennessee	283
19	Mississippi	278
20	Missouri	274
21	Alabama	243
22	Colorado	226
23	Nevada	221
24	Kansas	220
25	Minnesota	219
26	Louisiana	203
27	Michigan	201
28	Texas	201
29	Ohio	194
30	North Carolina	186
31	South Carolina	186
32	Florida	176
33	West Virginia	142
34	Georgia	141
35	Arizona	136
36	Massachusetts	131
37	Pennsylvania	129
38	Virginia	127
39	Connecticut	126
40	New Jersey	119
41	California	116
42	Delaware	113
43	Puerto Rico	113
44	Maryland	100
45	Washington	89
46	New York	83
47	Oregon	81
48	District of Columbia	67
49	Hawaii	61
50	New Hampshire	60
51	Maine	25
52	Vermont	21

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,830
2	New York	1,697
3	Massachusetts	1,423
4	Connecticut	1,281
5	Louisiana	1,247
6	Rhode Island	1,107
7	Mississippi	1,085
8	District of Columbia	909
9	Arizona	804
10	Illinois	762
11	Florida	757
12	Michigan	747
13	South Carolina	729
14	Georgia	710
15	Delaware	688
16	Pennsylvania	676
17	Maryland	673
18	Texas	612
19	Indiana	603
20	Arkansas	587
21	Alabama	579
22	North Dakota	572
23	Nevada	563
24	Iowa	512
25	New Mexico	454
26	Missouri	445
27	Ohio	441
28	California	436
29	Tennessee	436
30	Minnesota	417
31	Virginia	412
32	South Dakota	392
33	North Carolina	391
34	Colorado	386
35	New Hampshire	345
36	Kansas	326
37	Kentucky	322
38	Idaho	313
39	Washington	313
40	Oklahoma	308
41	Nebraska	304
42	Wisconsin	297
43	Montana	261
44	Puerto Rico	245
45	West Virginia	233
46	Utah	175
47	Oregon	153
48	Hawaii	144
49	Wyoming	117
50	Maine	108
51	Vermont	92
52	Alaska	87

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Montana	11
2	North Dakota	10
3	Kansas	9
4	South Dakota	9
5	Iowa	7
6	Arkansas	6
7	Mississippi	6
8	South Carolina	6
9	Wisconsin	6
10	Wyoming	6
11	Indiana	5
12	Hawaii	4
13	Idaho	4
14	Kentucky	4
15	Missouri	4
16	North Carolina	4
17	Oklahoma	4
18	Rhode Island	4
19	Tennessee	4
20	Alabama	3
21	Florida	3
22	Illinois	3
23	Michigan	3
24	Minnesota	3
25	Nebraska	3
26	West Virginia	3
27	California	2
28	Georgia	2
29	Louisiana	2
30	Massachusetts	2
31	Nevada	2
32	New Mexico	2
33	Ohio	2
34	Pennsylvania	2
35	Texas	2
36	Virginia	2
37	Arizona	1
38	Connecticut	1
39	Delaware	1
40	Maryland	1
41	New Jersey	1
42	Oregon	1
43	Puerto Rico	1
44	Utah	1
45	Washington	1
46	Alaska	0
47	Colorado	0
48	District of Columbia	0
49	Maine	0
50	New Hampshire	0
51	New York	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	175,829	1	99
Chattahoochee	Georgia	159,072	2	99
Trousdale	Tennessee	155,175	3	99
Lafayette	Florida	150,202	4	99
Lake	Tennessee	138,968	5	99
Davidson	Tennessee	46,905	264	91
Richland	South Carolina	40,531	411	86
York	South Carolina	22,831	1452	53
Orange	California	18,798	1801	42
Pierce	Washington	11,809	2456	21

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Emporia city	Virginia	5,238	1	99
Hancock	Georgia	5,203	2	99
Kenedy	Texas	4,950	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	637	817	73
Davidson	Tennessee	475	1155	63
Orange	California	452	1218	61
York	South Carolina	356	1470	53
Pierce	Washington	269	1757	44

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.